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020 ▼a 9780438382213
035 ▼a (MiAaPQ)AAI10815943
035 ▼a (MiAaPQ)uiowa:15789
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 658
1001 ▼a Meng, Bo.
24510 ▼a Corporate Finance and Machine Learning.
260 ▼a [S.l.] : ▼b The University of Iowa., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 181 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: A.
500 ▼a Adviser: Anand M. Vijh.
5021 ▼a Thesis (Ph.D.)--The University of Iowa, 2018.
520 ▼a In this dissertation, I study corporate activities, and their predictive abilities of market returns.
520 ▼a The first chapter examines the determinants of industry merger waves. We propose a continuous merger activity variable (MAV) as an alternative to discrete industry merger waves. We find that the ranking of MAV within a quarter is associated with
520 ▼a The second chapter examines the predictive ability of many corporate activities, including mergers and acquisitions, insider trading, share repurchases, etc. Using machine learning approaches, we find that an aggregate index of corporate activit
520 ▼a The third chapter examines the relationship between firm valuation and takeover activity, using the European debt crisis as a laboratory. The European debt crisis in mid-2011 caused a wide-spread redemption of money market mutual funds (MMFs) wi
590 ▼a School code: 0096.
650 4 ▼a Finance.
690 ▼a 0508
71020 ▼a The University of Iowa. ▼b Business Administration.
7730 ▼t Dissertation Abstracts International ▼g 80-02A(E).
773 ▼t Dissertation Abstract International
790 ▼a 0096
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998208 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자